Novel CaCO3-Based Material Formulation for Orange-Colored Spectrum Tracer Projectile
Bibliographic record
Abstract
This study underlines the development of orange-colored spectrum tracer material based on Fe/CaCO3/PVC compound. The optimum composition of the compound was determined by comparing three different ratios of Fe-CaCO3-PVC. The sample with the highest CaCO3 content decomposed earlier than that of the other samples. This sample also produced the lowest calorific energy around 275.61 cal/g and emitted a medium-dark orange spectrum. The color brightness was affected by the number of color source materials as well as CaCl2 and Ca(OH)2 which were formed during the combustion process. Meanwhile, the sample with the lowest CaCO3 content produced light orange color with a sharp spectrum emission. The concentration of fuel material (Fe) as well as the higher calorific energy (466.39 cal/g) of this composition contributes to the enhanced spectrum intensity. All samples emitted the orange color, with the wavelength ranging from 585 to 593 nm. Beside of calcite as the main phase, the x-ray diffraction analysis at 650 ℃ shows Fe2O3 as a secondary phase. The FTIR analysis of the as-heated sample presents the Fe-O bending band with a broader peak, which can be attributed to the existence of Fe2O3 species. The presence of this structure made all these samples have high ignition temperature. HIGHLIGHTS The tracer formulation contain iron, CaCO3 and polyvinyl chloride Iron fuel influences the thermal stability of tracer material The tracer composition emits orange-color start from 587 to 594 nm wavelength The highest spectrum intensity found at composition with containing greatest iron fuel GRAPHICAL ABSTRACT
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".